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Generalized from Karpathy's autoresearch. Same loop, any domain.
An AI agent runs an infinite hill-climbing loop: modify → run → measure → keep or revert → repeat. No human in the loop. Wake up to a TSV of completed experiments.
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| #define _XOPEN_SOURCE 700 | |
| #include <signal.h> | |
| #include <unistd.h> | |
| int main() | |
| { | |
| sigset_t set; | |
| int status; | |
| if (getpid() != 1) return 1; |
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| .markdown-here-wrapper { | |
| font-size: 16px; | |
| line-height: 1.8em; | |
| letter-spacing: 0.1em; | |
| } | |
| pre, code { | |
| font-size: 14px; | |
| font-family: Roboto, 'Courier New', Consolas, Inconsolata, Courier, monospace; |
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| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
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